dragonFirst, we create a dataset containing element network information joined with other attributes of elements. We specifically only consider elements that are known to form at least five minerals on Earth since 4.33 Ga. The following elements are therefore excluded (in parentheses is given the number of minerals it forms): Dy (1), Er (1), Gd (1), Hf (1), Sm (1), Re (2), Rb (3).
## # A tibble: 66 × 16
## element n_elements n_minerals n_localities element_name element_hsab
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 Ag 28 172 3166 Silver Soft acid
## 2 Al 52 881 10322 Aluminum Hard acid
## 3 As 59 634 4664 Arsenic Hard acid
## 4 Au 12 31 5616 Gold Soft acid
## 5 B 42 232 1295 Boron Soft acid
## 6 Ba 40 218 2904 Barium Hard acid
## 7 Be 36 121 1352 Beryllium Hard acid
## 8 Bi 40 212 1821 Bismuth Int. acid
## 9 Br 15 13 88 Bromine Soft base
## 10 C 50 417 9650 Carbon Soft base
## # … with 56 more rows, and 10 more variables: atomic_mass <dbl>,
## # number_of_protons <dbl>, element_table_period <dbl>,
## # element_table_group <dbl>, atomic_radius <dbl>, pauling <dbl>,
## # element_metal_type <chr>, element_density <dbl>,
## # element_specific_heat <dbl>, element_crust_percent_weight <dbl>
First, we explore whether we should likely transform an axis on
a log-scale. Model diagnostics are shown below for the model with
log y and untransformed x, whose diagnostics best meet
assumptions.
The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.
First, we explore whether we should likely transform an axis on
a log-scale. Model diagnostics are shown below for the model with
log y and untransformed x, whose diagnostics best meet
assumptions.
The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.
Note that there are six elements which do not appear in this analysis because they are missing crust data - C, H, N, REE (rare earth elements), Rh, Te.
First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log x and untransformed y, whose diagnostics best meet assumptions.
The resulting model is as follows - THERE IS A POSITIVE RELATIONSHIP.
First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with both untransformed y and x, whose diagnostics best meet assumptions.
The resulting model is as follows - THERE IS NO RELATIONSHIP.:
First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with log y and untransformed x, whose diagnostics best meet assumptions.
The resulting model is as follows - THERE IS NO RELATIONSHIP.
First, we explore whether we should likely transform an axis on a log-scale. Model diagnostics are shown below for the model with both axes untransformed; all diagnostics are about the same, so we’ll use the regular data.
The resulting model is as follows - THERE IS A NEGATIVE RELATIONSHIP.
Here we export figures to PDF files for use in manuscript.
Figure 1 is two panels comprised of model1_plot and
model2_plot.
Figure 2 is two panels comprised of model3_plot and
model6_plot.
The following shows the R version and package versions loaded for this analysis to enable reproducibility.
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cowplot_1.1.1 ggrepel_0.9.1 ggtext_0.1.1 performance_0.8.0
## [5] broom_0.7.12 dragon_1.2.0 forcats_0.5.1 stringr_1.4.0
## [9] dplyr_1.0.8 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
## [13] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-3 ellipsis_0.3.2 rprojroot_2.0.2
## [4] markdown_1.1 fs_1.5.2 gridtext_0.1.4
## [7] rstudioapi_0.13 roxygen2_7.1.2 farver_2.1.0
## [10] remotes_2.4.2 golem_0.3.1 fansi_1.0.2
## [13] lubridate_1.8.0 xml2_1.3.3 splines_4.1.2
## [16] knitr_1.37 config_0.3.1 pkgload_1.2.4
## [19] jsonlite_1.8.0 dbplyr_2.1.1 shinydashboard_0.7.2
## [22] shiny_1.7.1 compiler_4.1.2 httr_1.4.2
## [25] backports_1.4.1 assertthat_0.2.1 Matrix_1.4-0
## [28] fastmap_1.1.0 cli_3.2.0 later_1.3.0
## [31] htmltools_0.5.2 prettyunits_1.1.1 tools_4.1.2
## [34] igraph_1.2.11 gtable_0.3.0 glue_1.6.2
## [37] Rcpp_1.0.8 cellranger_1.1.0 jquerylib_0.1.4
## [40] vctrs_0.3.8 nlme_3.1-155 insight_0.16.0
## [43] xfun_0.29 ps_1.6.0 brio_1.1.3
## [46] testthat_3.1.2 rvest_1.0.2 mime_0.12
## [49] lifecycle_1.0.1 scales_1.1.1 ragg_1.2.2
## [52] hms_1.1.1 promises_1.2.0.1 yaml_2.3.5
## [55] see_0.6.9 sass_0.4.0 stringi_1.7.6
## [58] highr_0.9 bayestestR_0.11.5 desc_1.4.0
## [61] pkgbuild_1.3.1 attempt_0.3.1 systemfonts_1.0.4
## [64] rlang_1.0.1 pkgconfig_2.0.3 evaluate_0.15
## [67] lattice_0.20-45 patchwork_1.1.1 labeling_0.4.2
## [70] processx_3.5.2 tidyselect_1.1.2 magrittr_2.0.2
## [73] R6_2.5.1 generics_0.1.2 DBI_1.1.2
## [76] pillar_1.7.0 haven_2.4.3 withr_2.4.3
## [79] mgcv_1.8-39 datawizard_0.2.3 modelr_0.1.8
## [82] crayon_1.5.0 shinyWidgets_0.6.4 utf8_1.2.2
## [85] tzdb_0.2.0 rmarkdown_2.11 usethis_2.1.5
## [88] grid_4.1.2 readxl_1.3.1 callr_3.7.0
## [91] reprex_2.0.1 digest_0.6.29 xtable_1.8-4
## [94] httpuv_1.6.5 textshaping_0.3.6 munsell_0.5.0
## [97] dockerfiler_0.1.4 bslib_0.3.1